Dr Rashel Sarkar, Dr. Sunil Mishra, Dr.Chethan S , Dr. Harish Morwani, Shaikh Sarfaraj , Dr. Meenakshi Sharma


Artificial Intelligence (AI) holds the capacity to transform various facets of business operations. Artificial intelligence (AI) can be used to discover supply chain failures, estimate demand, enhance operations and transportation routes, and evaluate data. In today's digital world, artificial intelligence (AI) seeks to offer prompt data access and wise counsel in progressively complicated financial scenarios. Critical information examination for authoritative restoration is stimulating scholarly interest in information examination. Regardless of the rising utilization of enormous data analysis for direction, shockingly little is had some significant awareness of how data management abilities improve data encounters for supply chain sustainability and the rehashing cycle. Specialists say that organizations normally use huge data analysis and artificial intelligence (AI) to conjecture the future advancements of the supply chain 4.0 business areas. Thus, a sample of ninety people was gathered for the relevant inquiry in order to generate quantitative evidence utilizing measurable methods. The review uses explicit factor evaluation, association analysis, and relapse investigation to define the goals. Artificial intelligence has the potential to significantly improve stock management, security, operational expenses, and distribution center efficiency, according to research. Overall, the study finds that throughout the information-gathering phase, artificial intelligence will have a significant impact on the supply chain. Creating fresh open doors for businesses in all sectors is typical. Implementing AI can help supply chains become even more productive and agile by providing insights into any disruptions ahead of time and helping to mitigate them. Additionally, AI may help with process upgrades throughout the entire supply chain organization and with identifying new opportunities.

Keyword : AI-Powered, Innovations, Revolutionising, Supply Chain Management, Artificial Intelligence

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February 15, 2024
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1. Aldoseri, A., Al-Khalifa, K., & Hamouda, A. (2023). A Roadmap for Integrating Automation with Process Optimization for AI-powered Digital Transformation. 2. Borodavko. B, Illés. B, and Bányai, "Role of Artificial Intelligence in Supply Chain," Academic Journal of Manufacturing Engineering, vol. 19, no. 1, pp. 75-79, 2021. 3. Boute, R. N., & Udenio, M. (2022). AI in logistics and supply chain management. In Global Logistics and Supply Chain Strategies for the 2020s: Vital Skills for the Next Generation (pp. 49-65). Cham: Springer International Publishing. 4. Darvazeh et al., "Big Data Analytics and its Applications in Supply Chain Management," New Trends in the Use of Artificial Intelligence for the Industry 4.0, 2020. 5. Farooq, M., Cheng, J., Khan, N. U., Saufi, R. A., Kanwal, N., & Bazkiaei, H. A. (2022). Sustainable Waste Management Companies with Innovative Smart Solutions: A Systematic Review and Conceptual Model. Sustainability, 14(20), 13146. 6. G. Baryannis et al., "Supply Chain Risk Management and Artificial Intelligence: State of the Art and Future Research Directions," International Journal of Production Research, vol. 57, no. 7, pp. 2179-2202, 2019. 7. He, R., Li, X., Chen, G., Chen, G., & Liu, Y. (2020). Generative adversarial network-based semi-supervised learning for real-time risk warning of process industries. Expert Systems with Applications, 150, 113244. 8. Irfan, I., Sumbal, M. S. U. K., Khurshid, F., & Chan, F. T. (2022). Toward a resilient supply chain model: critical role of knowledge management and dynamic capabilities. Industrial management & data systems, 122(5), 1153-1182. 9. Kalasani, R. R. (2023). An Exploratory Study of the Impacts of Artificial Intelligence and Machine Learning Technologies in the Supply Chain and Operations Field (Doctoral dissertation, University of the Cumber lands). 10. Lal, K., Ballamudi, V. K. R., & Thaduri, U. R. (2018). Exploiting the Potential of Artificial Intelligence in Decision Support Systems. ABC Journal of Advanced Research, 7(2), 131-138. 11. M. Brown, "Artificial Intelligence Data-Driven Internet of Things Systems, Real-Time Process Monitoring, and Sustainable Industrial Value Creation in Smart Networked Factories," Journal of Self-Governance and Management Economics, vol. 9, no. 2, pp. 21-31, 2021. 12. Ma, L., & Sun, B. (2020). Machine learning and AI in marketing–Connecting computing power to human insights. International Journal of Research in Marketing, 37(3), 481-504. 13. P. Dauvergne, "Is Artificial Intelligence Greening Global Supply Chains? Exposing the Political Economy of Environmental Costs," Review of International Political Economy, vol. 29, no. 3, pp. 696-718, 2022. 14. R. Dubey et al., "Facilitating Artificial Intelligence Powered Supply Chain Analytics Through Alliance Management During the Pandemic Crises in the B2B Context," Industrial Marketing Management, vol. 96, pp. 135-146, 2021. 15. Spaniol, M. J., & Rowland, N. J. (2023). AI‐assisted scenario generation for strategic planning. Futures & Foresight Science, e148. 16. Surajit Bag et al., "Big Data Analytics and Artificial Intelligence Technologies Based Collaborative Platform Empowering Absorptive Capacity in Health Care Supply Chain: an Empirical Study," Journal of Business Research, vol. 154, 2023. 17. T. H. Davenport, "From Analytics to Artificial Intelligence," Journal of Business Analytics, vol. 1, no. 2, pp. 73-80, 2018. 18. Usama awan et al., "Artificial Intelligence for Supply Chain Success in the Era of Data Analytics," The Fourth Industrial Revolution: Implementation of Artificial Intelligence for Growing Business Success, vol. 935, pp. 3-21, 2021. 19. Weber, F. D., & Schütte, R. (2019). State-of-the-art and adoption of artificial intelligence in retailing. Digital Policy, Regulation and Governance, 21(3), 264-279. 20. Yablonsky, S. (2022). AI-Driven Innovation: Towards a Conceptual Framework. In Artificial Intelligence and Innovation Management (pp. 97-116).